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Abstract:

A method for obtaining operating parameters of a power plant includes
data mining a historical operating condition database for the power plant
with a partitional clustering algorithm to generate a statistical model,
and calculating dynamic operating condition target values from the
statistical model taking into account current operating condition data of
the power plant. The method further includes performing a real-time
energy-loss analysis based on the dynamic operating condition target
values and automatically identifying at least one operating parameter of
the power plant from the energy-loss analysis. The partitional clustering
algorithm can be a k-means clustering algorithm.

Claims:

1. A method for obtaining operating parameters of a power plant,
comprising: data mining a historical operating condition database for the
power plant with a first partitional clustering algorithm to generate a
statistical model; calculating dynamic operating condition target values
from the statistical model taking into account current operating
condition data of the power plant; performing a real-time energy-loss
analysis based on the dynamic operating condition target values, and
automatically identifying at least one operating parameter of the power
plant from the energy-loss analysis.

2. The method of claim 1, wherein the power plant comprises a fossil fuel
power plant.

3. The method of claim 2, wherein the fossil fuel power plant comprises a
coal fired power plant.

5. The method of claim 4, wherein the step of calculating further
comprises: performing a second partitional clustering algorithm upon the
statistical model and the current operating condition data of the power
plant.

7. The method of claim 6, wherein the step of calculating further
comprises sorting operating condition target values resulting from the
k-means clustering algorithm using a bubble sort algorithm.

8. The method of claim 6, wherein the step of performing a real-time
energy-loss analysis further comprises: analyzing boiler efficiency based
on the dynamic operating condition target values.

9. The method of claim 6, wherein the step of performing a real-time
energy-loss analysis further comprises: using an equivalent enthalpy drop
method based on the dynamic operating condition target values.

10. The method of claim 6, wherein the step of performing a real-time
energy-loss analysis further comprises: analyzing a steam turbine heat
rate based on the dynamic operating condition target values.

12. A method for obtaining operating parameters of a power plant,
comprising: performing a first k-means clustering algorithm upon
historical operating condition data of the power plant to generate a
statistical model; calculating dynamic operating condition target values
in real-time by performing a k-means clustering algorithm upon the
statistical model and current operating condition data of the power
plant; performing real-time energy-loss analysis based on the dynamic
operating condition target values; and automatically identifying at least
one operating parameter of the power plant from the energy-loss analysis.

13. The method of claim 12, wherein the step of calculating further
comprises: performing a second partitional clustering algorithm upon the
statistical model and the current operating condition data of the power
plant.

14. A system for obtaining operating parameters of a power plant,
comprising: a non-transitory machine readable storage for storing
historical operating condition data for the power plant; a processor
configured for: data mining a historical operating condition database for
the power plant with a partitional clustering algorithm to generate a
statistical model; calculating dynamic operating condition target values
from the statistical model taking into account current operating
condition data of the power plant; performing a real-time energy-loss
analysis based on the dynamic operating condition target values, and
automatically identifying at least one operating parameter of the power
plant from the energy-loss analysis.

15. The system of claim 14, further comprising: an interface for
displaying the at least one operating parameter of the power plant.

16. The system of claim 14, further comprising: one or more sensors
communicably coupled with the power plant, wherein said one or more
sensors garner and transmit data associated with the power plant.

17. The system of claim 16, wherein the processor is further configured
for receiving data associated with the power plant from the one or more
sensors and calculating current operating condition data of the power
plant based on the data that was received.

18. The system of claim 14, further comprising: one or more adjustment
devices coupled with the power plant, wherein the one or more adjustment
devices adjust the one or more operating parameters of the power plant.

19. The system of claim 18, wherein the processor is further configured
for sending commands to the one or more adjustment devices to adjust one
or more operating parameters of the power plant.

20. The system of claim 14, wherein the partitional clustering algorithm
comprises a k-means clustering algorithm.

Description:

FIELD

[0001] Disclosed embodiments relate to energy production, and more
particularly to the operation of power plants.

BACKGROUND

[0002] A Condition Monitoring System (CMS) is an automated system for
monitoring the parameters of machinery, such as a power plant, so as to
predict failures, perform maintenance and adjust performance. A
fossil-fuel power plant, for example, may use a CMS to aid an operator in
optimizing the performance of the power plant. One of the ways a CMS
accomplishes this task is by performing energy loss analysis to determine
the causes of energy loss.

[0003] An "operating condition" of a power plant refers to a premise upon
which the power plant is operating. Air temperature is an example of an
operating condition within a power plant. The operating conditions of a
power plant with the least equivalent fossil fuel consumption are
referred to as "optimal conditions" or "optimal operating conditions." A
power plant's optimal conditions may vary according to certain
characteristics of the power plant, such as current load, boundary
conditions, current fuel characteristics and current circumstances.
Energy-loss analysis quantifies and ranks the contribution of key
operating parameters to the equivalent fossil fuel consumption deviation,
i.e., energy loss. To that end, a CMS may conduct an energy-loss analysis
to identify the causes of the power plant's deviation from optimal
performance. Based on the causes identified by the CMS, an operator may
consequently take corresponding actions to adjust the operating
parameters of the power plant, so as to increase the power plant's
performance.

[0004] One of the key components of an energy-loss analysis is determining
what constitutes optimal operating conditions for the power plant. Target
values for optimal operating conditions are the inputs for energy-loss
analysis, which provides operational guidance. Known methods for
calculating target values for power plants include designed target
values, overhauled target values and off-design target values. Such known
approaches for determining target values for optimal operating
conditions, however, operate using only constant or static data and do
not take into account current variations in load, fuel characteristics or
present circumstances of the power plant. Additionally, the known
approaches can be time-consuming and expensive to implement. There is a
need for higher-accuracy energy-loss analysis for power plants that
accounts for current variations in operating parameters.

SUMMARY

[0005] Disclosed embodiments include methods for obtaining operating
parameters of a power plant. An example method includes data mining a
historical operating condition database for the power plant with a
partitional clustering algorithm to generate a statistical model, and
calculating dynamic operating condition target values from the
statistical model taking into account current operating condition data of
the power plant. The method further includes performing a real-time
energy-loss analysis based on the dynamic operating condition target
values, and automatically identifying at least one operating parameter of
the power plant from the energy-loss analysis. The partitional clustering
algorithm can comprise a k-means clustering algorithm.

[0006] A system for obtaining operating parameters of a power plant
comprises a non-transitory machine readable storage for storing
historical operating condition data for the power plant and a processor.
The processor is configured for data mining a historical operating
condition database for the power plant with a partitional clustering
algorithm to generate a statistical model, calculating dynamic operating
condition target values from the statistical model taking into account
current operating condition data of the power plant, performing a
real-time energy-loss analysis based on the dynamic operating condition
target values, and automatically identifying at least one operating
parameter of the power plant from the energy-loss analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 is an example illustration of a condition monitoring system
shown in conjunction with a power plant, according to an example
embodiment.

[0008] FIG. 2 is flow chart illustrating the control flow for the process
of performing energy-loss calculations for a power plant where the data
mining comprises k-means clustering according to an example embodiment.

[0009]FIG. 3 is a histogram illustrating experimental results gathered
according to an example embodiment.

[0010] FIG. 4 is a histogram illustrating a second set of experimental
results gathered according to an example embodiment.

DETAILED DESCRIPTION

[0011] Disclosed embodiments are described with reference to the attached
figures, wherein like reference numerals are used throughout the figures
to designate similar or equivalent elements. The figures are not drawn to
scale and they are provided merely to illustrate certain disclosed
aspects. Several disclosed aspects are described below with reference to
example applications for illustration. It should be understood that
numerous specific details, relationships, and methods are set forth to
provide a full understanding of the disclosed embodiments. One having
ordinary skill in the relevant art, however, will readily recognize that
the subject matter disclosed herein can be practiced without one or more
of the specific details or with other methods. In other instances,
well-known structures or operations are not shown in detail to avoid
obscuring certain aspects. This Disclosure is not limited by the
illustrated ordering of acts or events, as some acts may occur in
different orders and/or concurrently with other acts or events.
Furthermore, not all illustrated acts or events are required to implement
a methodology in accordance with the embodiments disclosed herein.

[0012] Disclosed embodiments include a method and system for performing
energy-loss analysis calculations in real-time. FIG. 1 is an example
illustration of a condition monitoring system (CMS) 100 shown in
conjunction with a power plant 102. CMS 100 includes at least one
processor 120, such as a digital signal processor. According to an
example embodiment, power plant 150 is a fossil fuel power plant, such as
a plant that burns coal, natural gas or petroleum (oil) to produce
electricity. The CMS 100 receives information about current operating
conditions and parameters of the power plant 150 from the sensors 102
that are configured for reporting information about the power plant 150.
The data received from the sensors 102, as well as any other data
calculated or generated by CMS 100, may be stored in database 106. Data
comprises non-transitory machine readable storage. In one disclosed
embodiment, operating condition data and operating parameters of the
power plant 150 garnered over defined periods of time may be stored in
database 106 so as to amass a historical record of the operating
condition data and operating parameters of the power plant 150.

[0013] In another disclosed embodiment, the database 106 also includes a
real-time database wherein current operating condition data, are stored
and accessed according to real-time constraints. Current operating
condition data is real-time data, i.e., current operating condition data
denotes operating condition data that is delivered immediately after
collection and wherein there is no delay in the timeliness of the
information provided. As described more fully below with reference to the
process of FIG. 2, in this embodiment database 106 stores current
operating condition data that is garnered from sensors 102 which are used
to conduct energy-loss analysis calculations. Historical operating
condition data is operating condition data is no longer, real-time data
and therefore applies to the past.

[0014] The CMS 100 may display the data garnered from the power plant 150
to the operator 110 via an interface 103, which may include a display, a
keyboard, mouse, touch screen, a camera, a microphone and speakers. The
interface 103 may further allow the operator 110 to control adjustment
devices 104, which adjusts various operating parameters of the power
plant 150, such as actuators. Operating parameters refer to adjustable
properties of the power plant 150. Main steam pressure is an example of
an operating parameter of the power plant.

[0015] FIG. 2 is flow chart illustrating an example flow for a method 200
for obtaining operating parameters for a power plant 150, including data
mining using a k-means clustering, according to an example embodiment. In
a first step 201, a database is populated with historical operating
condition data pertaining to the power plant 150. In step 202, data
mining of the historical data in database 106 is executed using a
partitional clustering algorithm, so as to generate a statistical model.

[0016] Clustering comprises the assignment of operating conditions in the
database into subsets (called clusters) so that observations in the same
cluster are similar in some sense. Clustering is a method of unsupervised
learning, and is a technique for statistical data analysis. A partitional
clustering algorithm calculates all clusters at once. The partitional
clustering algorithm can comprise a k-means clustering, a k-means
derivative such as fuzzy c-means clustering or a QT clustering algorithm,
a locality-sensitive hashing, or graph-theoretic methods, so as to
produce a first cluster set.

[0017] K-means clustering is a method of cluster analysis which aims to
partition n operating conditions, (x1, x2, . . . , xn),
into k sets or clusters (k≦n), S={S1, S2, . . . ,
Sk}, in which each operating condition belongs to the cluster with
the nearest mean, as defined in the formula below:

arg min s i = 1 k x j .di-elect cons. S i
x j - μ i 2 ##EQU00001##

wherein μi is the mean of points in Si.

[0018] One implementation of the k-means clustering algorithm is executed
as follows. Given an initial set of k means m1.sup.(1), . . . ,
mk.sup.(1), which may be specified randomly or by some heuristic,
the k-means clustering algorithm proceeds by alternating between the
following two steps: 1) an assignment step wherein each operating
condition, (x1, x2, . . . , xn), is assigned to the
cluster with the closest mean according to the following formula:

wherein t represents an increment indicator. The two steps above are
continually executed until there is no change in an assignment step. The
algorithm is deemed to have converged when the assignments no longer
change.

[0019] Further to step 202, the equation below can be employed to measure
the similarity between operation conditions at time i and operating
conditions at some other time j:

wherein {circumflex over (x)}ik and {circumflex over (x)}jk are
normalized values of the kth operating condition at time i and j,
respectively. Further, wk is a user-defined weighting factor for the
kth operating condition. N is the number of operating conditions for
a particular class. The equation above is used to calculate the proximity
of the same operating condition at a different time. In one embodiment,
the equation above is used to implement a rule wherein only those
historical operating conditions within a predefined proximity to current
operating conditions may be used in the calculation of step 206 below.

[0020] In step 204, current operating condition data of the power plant
150 is garnered in real-time from the sensors 102 and is provided to a
processor 120 running a disclosed algorithm. In step 206, dynamic optimal
operating condition target values for operating parameters of the power
plant 150 are calculated in real-time from the statistical model taking
into account current operating condition data of the power plant received
in step 204.

[0021] In one embodiment step 206 comprises executing an update step of
the k-means clustering algorithm wherein the current operating condition
data is deemed to be the centroid of the operating conditions in the
clusters calculated in step 202. Subsequently, the assignment and update
steps of the k-means clustering algorithm are continually executed until
there is no change in an assignment step. The resulting centroid values
of the converged clusters are candidates for optimal operating condition
target values for operating parameters of the power plant 150. Then, the
candidate operating condition target values calculated above are ranked
by importance.

[0022] An example method of ranking the order of the candidate operating
condition target values comprises bubble sorting. A bubble sort is a
simple sorting algorithm that works by repeatedly stepping through a list
to be sorted, comparing each pair of adjacent items and swapping them if
they are in the wrong order. The pass through the list is repeated until
no swaps are needed, which indicates that the list is sorted. In this
case, the bubble sort repeatedly steps through the candidate operating
condition target values to be sorted, with the goal of ranking the values
according to importance. Each pair of values is compared and swapped if
they are in the wrong order and the comparisons continue until no swaps
are necessary. One measure of importance for an operating condition
target value is a thermo-economic indicator, which is related to a
condition's affect on energy loss. In the case of a coal-fired power
plant, unit coal consumption rate, for example, may be used as the
thermo-economic indicator, though other thermo-economic indicators may be
used. Therefore, the candidate operating condition target value
correspondent to the lowest unit coal consumption rate is the highest
ranked target value and is therefore deemed an optimal operating
condition target value.

[0023] In step 208, energy-loss analysis calculations are performed based
on the optimal operating condition target values calculated in step 206.
Various methods may be used to identify those operating parameters
affecting energy loss. Heat loss due to boiler efficiency, for example,
may be analyzed to determine which parameters, such as exhaust gas, air
imperfections, fuel imperfections, surface radiation, surface convection
and heat refuse, are contributing to heat loss, and therefore energy
loss. In another example, the equivalent enthalpy drop method, a partial
quantitative analysis method, may be used to identify lossy operating
parameters. Additionally, the steam turbine heat rate of the power plant
may be analyzed to determine which parameters are contributing to energy
loss. The result of step 208 is the identification of operating
parameters of the power plant 150 that currently account for energy-loss,
according to the energy-loss calculations. A variety of operating
parameters of power plant 150 may contribute to energy loss.

[0024] With regard to a fossil fuel power plant, the performance of the
plant is generally affected by the main initial and final operating
parameters, such as main steam pressure, main steam temperature, reheat
steam temperature, exhaust steam pressure and the final feed water
temperature. Also, operating parameters such as exhaust gas temperature,
oxygen percentage in exhaust gas, carbon percentage in fly ash and carbon
percentage in slag can affect energy losses in boiler combustion.
Further, the following operating parameters pertaining to the
thermodynamic cycle in the steam turbine and feedwater regenerative
system generally affect energy loss: terminal temperature difference
(TTD) of both the high pressure heaters and the low pressure heaters,
subcooling of the condensate water and the inlet steam flow rate of
auxiliary turbine for boiler feedwater pump, and reheat attemperation
flow rate.

[0025] In step 210, operating parameters of the power plant 150 that
currently account for energy-loss, according to the energy-loss
calculations of step 208, are identified. In step 212, the information
generated in step 210 is displayed for the operator 110, such as via
interface 103. In step 214, the CMS 100, either automatically or in
response to receiving instructions from the operator 110 via interface
103, sends one or more commands to adjustment devices 104 to adjust a
working parameter of power plant 150, responsive to steps 210 and 212. In
step 216, there is a time delay, after which control flows back to step
204, wherein new current operating condition data is received and steps
204-214 are executed once more.

[0026] Disclosed embodiments are further illustrated by the following
specific examples showing experimental results generated using disclosed
methods, which should not be construed as limiting the scope or content
of this Disclosure in any way.

[0027] In a first example, shown in FIG. 3, a histogram 302 illustrates
experimental results gathered according to an example embodiment
implemented at a fossil fuel steam turbine plant. The data of FIG. 3
relates to two units of a fossil fuel steam turbine plant running at full
capacity, wherein each unit is a sub-critical turbine unit with 350 MW of
power output. The y-axis lists, operating parameters of the power plant,
such as exhaust steam pressure and reheat steam temperature. Each
operating parameter's contribution to energy loss, as per the energy loss
analysis (ELA), is shown on the x-axis of the histogram in standardized
units.

[0028]FIG. 3 shows that that heater 304 contributes the most to energy
loss, potentially because the enthalpy rise setting of the heater is set
to a lower value than the optimal setting. FIG. 3 also shows that that
heater 306 contributes the second-most to energy loss, potentially
because the enthalpy rise setting of the heater is set to a higher value
than the optimal setting. Note that a fossil fuel power plant may have
multiple heaters that heat various media in different stages of the
power-generation process. Consequently, each heater may produce a unique
result when performing energy loss analysis. Contributing third and
fourth-most are the boiler excess oxygen 308 and reheat spray flow 310
parameters, potentially because the parameters are set to a higher value
than the optimal setting. The histogram of FIG. 3 can be displayed for an
operator, such as operator 110, who can use this data to manipulate
adjustment devices 104 so as to adjust those operating parameters of the
power plant that need adjusting the most. The above scenario would lead
to operation of the power plant at a higher efficiency rate. Experimental
data calculations show that operating the power plant at a higher
efficiency rate may lead to substantial cost savings.

[0029] In a second example, shown in FIG. 4, a histogram 402 illustrates
experimental results gathered according to an example embodiment
implemented at a fossil fuel steam turbine plant. The data of FIG. 4
relates to two units of a fossil fuel steam turbine plant running at full
capacity, wherein each unit is a super-critical turbine unit with 600 MW
of power output.

[0030] FIG. 4 shows that that heater 404 contributes the most to energy
loss, potentially because the enthalpy rise setting of the heater is set
to a higher value than the optimal setting. FIG. 4 also shows that that
heater 406 contributes the second-most to energy loss, potentially
because the enthalpy rise setting of the heater is set to a lower value
than the optimal setting. Contributing third and fourth-most are the
exhaust steam pressure 408 and reheat steam pressure 410 parameters,
because the parameters are not set to optimal settings. Again, the
histogram of FIG. 4 can be displayed for an operator, who can use this
data to manipulate adjustment devices 104 so as to operate the power
plant at a higher efficiency rate. Experimental data calculations show
that operating the aforementioned power plant at a higher efficiency rate
may lead to a substantial savings.

[0031] The experimental data of FIG. 3 and FIG. 4 above show the ability
of the disclosed methods to provide energy loss analysis that is specific
and customized to different types of power plants. This results in an
energy loss analysis method that is versatile and portable for use with
varied types of power plants and fuels.

[0032] While various disclosed embodiments have been described above, it
should be understood that they have been presented by way of example
only, and not limitation. Numerous changes to the subject matter
disclosed herein can be made in accordance with this Disclosure without
departing from the spirit or scope of this Disclosure. In addition, while
a particular feature may have been disclosed with respect to only one of
several implementations, such feature may be combined with one or more
other features of the other implementations as may be desired and
advantageous for any given or particular application.

[0033] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting. As used
herein, the singular forms "a," "an," and "the" are intended to include
the plural forms as well, unless the context clearly indicates otherwise.
Furthermore, to the extent that the terms "including," "includes,"
"having," "has," "with," or variants thereof are used in either the
detailed description and/or the claims, such terms are intended to be
inclusive in a manner similar to the term "comprising."

[0034] As will be appreciated by one skilled in the art, the subject
matter disclosed herein may be embodied as a system, method or computer
program product. Accordingly, this Disclosure can take the form of an
entirely hardware embodiment, an entirely software embodiment (including
firmware, resident software, micro-code, etc.) or an embodiment combining
software and hardware aspects that may all generally be referred to
herein as a "circuit," "module" or "system."Furthermore, this Disclosure
may take the form of a computer program product embodied in any tangible
medium of expression having computer usable program code embodied in the
medium.

[0035] Any combination of one or more computer usable or computer readable
medium(s) may be utilized. The computer-usable or computer-readable
medium may be, for example, but not limited to, an electronic, magnetic,
optical, electromagnetic, infrared, or semiconductor system, apparatus,
or device. More specific examples (a non-exhaustive list) of the
computer-readable medium would include non-transitory media including the
following: an electrical connection having one or more wires, a portable
computer diskette, a hard disk, a random access memory (RAM), a read-only
memory (ROM), an erasable programmable read-only memory (EPROM or Flash
memory), a portable compact disc read-only memory (CDROM), an optical
storage device, or a magnetic storage device.

[0036] Computer program code for carrying out operations of the disclosure
may be written in any combination of one or more programming languages,
including an object-oriented programming language such as Java,
Smalltalk, C++ or the like and conventional procedural programming
languages, such as the "C" programming language or similar programming
languages. The program code may execute entirely on the user's computer,
partly on the user's computer, as a stand-alone software package, partly
on the user's computer and partly on a remote computer or entirely on the
remote computer or server. In the latter scenario, the remote computer
may be connected to the user's computer through any type of network,
including a local area network (LAN) or a wide area network (WAN), or the
connection may be made to an external computer (for example, through the
Internet using an Internet Service Provider).

[0037] The Disclosure is described below with reference to flowchart
illustrations and/or block diagrams of methods, apparatus (systems) and
computer program products according to embodiments of the invention. It
will be understood that each block of the flowchart illustrations and/or
block diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer program
instructions. These computer program instructions may be provided to a
processor of a general purpose computer, special purpose computer, or
other programmable data processing apparatus to produce a machine, such
that the instructions, which execute via the processor of the computer or
other programmable data processing apparatus, create means for
implementing the functions/acts specified in the flowchart and/or block
diagram block or blocks.

[0038] These computer program instructions may also be stored in a
physical computer-readable storage medium that can direct a computer or
other programmable data processing apparatus to function in a particular
manner, such that the instructions stored in the computer-readable medium
produce an article of manufacture including instruction means which
implement the function/act specified in the flowchart and/or block
diagram block or blocks.

[0039] The computer program instructions may also be loaded onto a
computer or other programmable data processing apparatus to cause a
series of operational steps to be performed on the computer or other
programmable apparatus to produce a computer implemented process such
that the instructions which execute on the computer or other programmable
apparatus provide processes for implementing the functions/acts specified
in the flowchart and/or block diagram block or blocks.